A dynamic neural network model for nonlinear system identification
A dynamic neural network model for nonlinear system identification
复制标题
非线性系统辨识的动态神经网络模型
DOI：
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发表时间：
2009
期刊：
影响因子：
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通讯作者：
Tsu
中科院分区：
文献类型：
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作者：
Chi;Pin;Ping;Tsu
本文在Hopfield神经网络的基础上，提出了一种新的动态神经网络来进行非线性系统的辨识。利用类李雅普诺夫准则进行收敛性分析，保证辨识过程中误差收敛。仿真结果表明，所提出的动态神经网络的李亚普诺夫方法训练可以获得良好的识别性能。
In this paper, a new dynamic neural network based on the Hopfield neural network is proposed to perform the nonlinear system identification. Convergent analysis is performed by the Lyapunov-like criterion to guarantee the error convergence during identification. Simulation results demonstrate that the proposed dynamic neural network trained by the Lyapunov approach can obtain good identified performance.